A fast and high-quality charge model for the next generation general AMBER force field

One-step antifouling coating of polystyrene using engineered polypeptides

Unwanted nonspecific adsorption caused by biomolecules influences the lifetime of biomedical devices and the sensing performance of biosensors. Previously, we have designed B-M-E triblock proteins that rapidly assemble on inorganic surfaces (gold and silica) and render those surfaces antifouling. The B-M-E triblock proteins have a surface-binding domain B, a multimerization domain M and an antifouling domain E. Many biomedical technologies involve organic (polymeric) surfaces where B-M-E triblock proteins could potentially be used. In this study, we computationally and experimentally investigate the assembly of B-M-E triblock proteins on polystyrene (PS) surfaces, using PS-binding peptides as a surface-binding block B. We used atomic force microscopy, dynamic light scattering, fluorescence microscopy and quartz crystal microbalance to test the antifouling coating functionality. We found that, like for inorganic surfaces, the B-M-E proteins with PS-binding peptides as B block, form homogeneous monomolecular layers on PS surfaces with good stability against PBS washing. The adsorbed protein layer fully prevents adsorption of fluorescently labeled bovine serum albumin to PS microfluidic chips. Similarly, no significant fouling was observed using quartz crystal microbalance when 1 % (v/v) or 10 % (v/v) human serum were used as foulants.

Repurposing FDA-approved drugs to target G-quadruplexes in breast cancer

Breast cancer, a leading cause of cancer-related mortality in women, is characterized by genomic instability and aberrant gene expression, often influenced by noncanonical nucleic acid structures such as G-quadruplexes (G4s). These structures, commonly found in the promoter regions and 5'-untranslated RNA sequences of several oncogenes, play crucial roles in regulating transcription and translation. Stabilizing these G4 structures offers a promising therapeutic strategy for targeting key oncogenic pathways. In this study, we employed a drug repurposing approach to identify FDA-approved drugs capable of binding and stabilizing G4s in breast cancer-related genes. Using ligand-based virtual screening and biophysical methods, we identified several promising compounds, such as azelastine, belotecan, and irinotecan, as effective G4 binders, with significant antiproliferative effects in breast cancer cell lines. Notably, belotecan and irinotecan exhibited a synergistic mechanism, combining G4 stabilization with their established topoisomerase I inhibition activity to enhance cytotoxicity in cancer cells. Our findings support the therapeutic potential of G4 stabilization in breast cancer, validate drug repurposing as an efficient strategy to identify G4-targeting drugs, and highlight how combining G4 stabilization with other established drug activities may improve anticancer efficacy.

Structure/function of ATP sulfurylase domain of human 3'-phosphoadenosine 5'-phosphosulfate synthase (hPAPSS)

3'-phosphoadenosine 5'-phosphosulfate (PAPS) is synthesized by PAPS synthase (PAPSS) in two steps. In the first step ATP sulfurylase (ATPS) transfers sulfate group onto adenylyl moiety of ATP to form adenosine 5'-phosphosulfate (APS) and PPi. APS-kinase (APSK) then transfers the gamma-phosphoryl from ATP onto 3'-OH of APS to form PAPS and ADP. Mutations of histidine's (H425/H428) of hPAPSS isoform1 knocked out ATPS and not APSK. In silico ATP binding and molecular dynamics experiments exhibited an unfavorable binding energy for mutant enzymes. Thus, requirements of H425NGH428 motif for ATPS is established. The N426 residue in various organisms is substituted with R. We mutated hPAPSS1 with basic residue K. The N426 to K426 (N-K) mutant exhibited slightly lower Km (3.7 mM) and higher Vmax (3X) for ATP compared to wildtype (WT, Km 4.3 mM). The Km for sulfate for N-K mutant was nearly same as WT but the Vmax was ∼4X higher for N-K. The catalytic efficiency (Vmax/Km) of N-K was ∼3 fold higher than WT. The full length hPAPSS1 evinced bimodal response against ATP, a paradigm that was deduced to be a trait of PAPSS that requires 2 mol of ATP/PAPS formed. This bimodal kinetics with ATP was lost when the N-terminal APSK was deleted from the C-terminal ATPS domain. The C-terminal domain contained ATPS activity, exhibited Km of 2.2 mM for ATP and Km of 0.53 mM for Sulfate and much higher catalytic efficiency compared to full length hPAPSS1. Thus, fused ATPS-APSK must be structurally and kinetically different than individual domains influenced by inter-domain residues.

Quantum Mechanics-Calibrated Classical Simulation of Earth-Abundant Divalent Metal Bis(trifluoromethanesulfonyl)imide Molar Conductivity in Organic Cosolvents with Onsager Transport Formalism

Molar conductivities are one of the fundamental transport properties of electrolytes that reflect the complex and dynamic interactions between ions and solvents comprehensively. The quantitative accuracy of experimental input-free simulations of molar conductivities is strongly influenced by the underlying interaction parameters employed in the model. When validated with experimental molar conductivities, the developed model could be used to reveal further atomistic level details about the solvation structures and correlated ion pair formation, providing in-depth knowledge about solution physical chemistry and shedding light on electrolyte-solvent system design rules. Divalent cations are more challenging to model than monovalent cations due to their higher charge densities and stronger interactions with the environment. Yet, they started attracting significant attention for next-generation energy storage purposes. In this work, we focus on two earth-abundant divalent cation electrolytes, Mg(TFSI)2 and Ca(TFSI)2 in a dimethylacetamide-tetrahydrofuran (DMA-THF) cosolvent system. We used quantum mechanical cluster models to optimize the force field parameters (including the pairwise nonbonded interaction parameters and atomic charges) to be applied in classical simulations. With the reliable force field model, we discussed the importance of including ion correlation explicitly in predicting the molar conductivities via the Onsager formalism and showed that the conventional Nernst-Einstein formula overestimates ionic mobilities due to its intrinsic independent and uncorrelated particle assumption. Further, we investigated the solvation structures and ion pair formations. We concluded that the suitability of the interaction potentials utilized in a classical model for particular systems needs to be assessed not solely by directly comparing the simulated molar conductivities with the measured ones but, more importantly, by using the correct formalism (Onsager) to deduce the simulated result from dynamics trajectories.

Impact of Fluctuations in the Peridinin-Chlorophyll a-Protein on the Energy Transfer: Insights from Classical and QM/MM Molecular Dynamics Simulations

The peridinin-chlorophyll a-protein is a light-harvesting complex found in dinoflagellates, which has an unusually high fraction of carotenoids. The carotenoids are directly involved in the energy transfer to chlorophyll with high efficiency. The detailed mechanism of energy transfer and the roles of the protein in the process remain debated in the literature, in part because most calculations have focused on a limited number of chromophore structures. Here we investigate the magnitude of the fluctuations of the site energies of individual and coupled chromophores, as the results are essential to the understanding of experimental spectra and the energy transfer mechanism. To this end, we sampled conformations of the PCP complex by means of classical and quantum mechanical/molecular mechanical (QM/MM) molecular dynamics simulations. Subsequently we performed (supermolecular) excitation energy calculations on a statistically significant number of snapshots using TD-LC-DFT/CAM-B3LYP and the semiempirical time-dependent long-range corrected density functional tight binding (TD-LC-DFTB2) as the QM method. We observed that the magnitude of the site energy fluctuations is large compared to the differences of the site energies between the chromophores, and this also holds for the coupled chromophores. We also investigated the composition of the coupled states, the effect of coupling on the absorption spectra, as well as transition dipole moment orientations and the possibility of delocalized states with Chl a. Our study thus complements previous computational studies relying on a single structure and establishes the most prominent features of the coupled chromophores that are essential to the robustness of the energy transfer process.

Artificial Intuition and accelerating the process of antimicrobial drug discovery

New drug development is a very challenging, expensive, and usually time-consuming process. This issue is very important with regard to antimicrobials, which are affected by the global issue of the development and spread of resistance. This framework underscores the urgency of accelerating drug development processes while reducing their costs. In this context, new bioinformatics tools can provide important support for drug development by limiting and shortening in vitro evaluation of the best outcomes, thereby minimizing costs. Recently, new Artificial Intelligence (AI)-based tools have been developed for de novo design of new molecules, or for the identification of features of inhibitors among a large set of molecules that can guide rational design. With this work, we present an Artificial Intuition (AI4)-based pharmacological analysis of a series of antimicrobial compounds that are known to be active against Mycobacterium tuberculosis. The compounds have been subjected to Molecular Dynamic Simulation (MDS), and the respective outputs processed with a Quantitative Complexity Management (QCM) tool in order to determine the corresponding complexity profiles. The comparison of different analogues in each series revealed a relationship between the complexity of the various chemical moieties and their importance for the biological activity of each compound, suggesting that QCM may be a useful tool in guiding the optimization process. This first attempt to apply the tool in the field of drug development has yielded interesting results, indicating that QCM, which powers AI4, can be implemented for rational drug design in the near future.

Substrate-specific effects point to the important role of Y361 as part of the YER motif in closing the binding pocket of OCT1

Organic cation transporter 1 (OCT1) is located in the sinusoidal membrane of human hepatocytes. It mediates the uptake of hydrophilic organic cationic drugs in hepatocytes and thus determine their systemic concentrations. OCT1 has a broad spectrum of structurally diverse substrates like metformin, sumatriptan, trospium, and fenoterol. Recent cryo-EM data suggested that Y361, E386, and R439, referred to as the YER motif, could be important for transport. Building on this, we used extensive functional analyses to investigate the general function, and the substrate-specific effects of the YER motif. We determined the activity of the Y361A, E386A, and R439A mutants for fifteen OCT1 substrates. Extended mutagenesis revealed the negative charge of E386 and the positive charge of R439 as essential for the transport of all substrates tested. Charge reversal mutants, E386R-R439E, did not restore transport activity, suggesting that at least one of the two amino acids is involved in additional interactions essential for transport. Y361 exhibited substrate-specific effects. The Y361A mutant transported fenoterol, but not pirbuterol or other beta2-adrenergic drugs with only one aromatic ring. MD simulations suggested that substrates with aromatic or lipophilic characteristics may compensate for the missing aromatic ring at position 361. Only tryptophan at codon 361 efficiently rescued the transport of the Y361A mutant supporting hydrogen bound interaction with E386 and R439. Our study confirms that the YER motif is essential for OCT1 transport and points to Y361 as a lever that interacts with E386 and R439 to trigger the closing of the binding pocket of human OCT1.

Targeting human prostaglandin reductase 1 with Licochalcone A: Insights from molecular dynamics and covalent docking studies

Prostaglandin reductase 1 (PTGR1) is an NADPH-dependent enzyme critical to eicosanoid metabolism. Its elevated expression in malignant tumors often correlates with poor prognosis due to its role in protecting cells against reactive oxygen species. This study explores the inhibitory potential of licochalcone A, a flavonoid derived from Xinjiang licorice root, on human PTGR1. Using molecular dynamics simulations, we mapped the enzyme's conformational landscape, revealing a low-energy, rigid-body-like movement of the catalytic domain relative to the nucleotide-binding domain that governs PTGR1's transition between open and closed states. Simulations of NADPH-depleted dimer and NADPH-bound monomer highlighted the critical role of intersubunit interactions and coenzyme binding in defining PTGR1's conformational landscape, offering a deeper understanding of its functional adaptability as a holo-homodimer. Covalent docking, informed by prior chemoproteomic cross-linking data, revealed a highly favorable binding pose for licochalcone A at the NADPH-binding site. This pose aligned with a transient noncovalent binding pose inferred from solvent site-guided molecular docking, emphasizing the stereochemical complementarity of the coenzyme-binding site to licochalcone A. Sequence analysis across PTGR1 orthologs in vertebrates and exploration of 3D structures of human NADPH-binding proteins further underscore the potential of the coenzyme-binding site as a scaffold for developing PTGR1-specific inhibitors, positioning licochalcone A as a promising lead compound.

Development of a Second-Generation, In Vivo Chemical Probe for PIKfyve

We optimized our highly potent and cell-active chemical probe for phosphatidylinositol-3-phosphate 5-kinase (PIKfyve), SGC-PIKFYVE-1, resulting in compounds with improved potency and demonstrated in vivo stability. Use of an in-cell, kinome-wide selectivity panel allowed for confirmation of excellent in-cell selectivity of our lead compound, 40, and another promising analogue, 46. Evaluation of the pharmacokinetic (PK) profiles of these two compounds revealed that both are well tolerated systemically and orally bioavailable. Coupled with its subnanomolar cellular potency and impressive selectivity in cells, the long half-life of 40 makes it an ideal candidate for the evaluation of the consequences of PIKfyve inhibition in vivo. PIKfyve inhibition has been investigated clinically for indications including rheumatoid arthritis, Crohn's disease, COVID-19, and ALS using a single compound (apilimod), supporting the development of orthogonal PIKfyve inhibitors with in vivo stability.

Partially Sulfated Pillar[5]Arenes: Synthesis and Molecular Recognition Properties

We report the synthesis and characterization of sulfated pillar[5]arene hosts (P5S2-P5S10) that differ in the number of sulfate substituents. All five P5Sn hosts display high solubility in water (73-131 mM) and do not undergo significant self-association according to 1H NMR dilution experiments. The x-ray crystal structures of P5S6, P5S6 ⋅ Me6HDA, P5S8 ⋅ Me6HDA, and P5S10 ⋅ Me6HDA reveal one intracavity molecule of Me6HDA and several external molecules of Me6HDA which form a network of close methonium ⋅ ⋅ ⋅ sulfate interactions. The thermodynamic parameters of complexation between P5Sn and the panel of guests was measured by direct or competitive isothermal titration calorimetry. We find that the binding free energy toward a guest becomes more negative as the number of sulfate substituents increase. Conversely, the binding free energy of a specific sulfated pillar[5]arene toward a homologous series of guests becomes more negative as the number of NMe groups increases. The ability to tune the host ⋅ guest affinity by changing the number of sulfate substituents will be valuable in supramolecular polymers, separation materials, and latching applications.

Tensor Train Optimization for Conformational Sampling of Organic Molecules

Exploring the conformational space of molecules remains a challenge of fundamental importance to quantum chemistry: identification of relevant conformers at ambient conditions enables predictive simulations of almost arbitrary properties. Here, we propose a novel approach, called TTConf, to enable conformational sampling of large organic molecules where the combinatorial explosion of possible conformers prevents the use of a brute-force systematic conformer search. We employ tensor trains as a highly efficient dimensionality reduction algorithm, effectively reducing the scaling from exponential to polynomial. In our approach, the conformational search is expressed as global energy minimization task in a high-dimensional grid of dihedral angles. Dimensionality reduction is achieved through a tensor train representation of the high-dimensional torsion space. The performance of the approach is assessed on a variety of drug-like molecules in direct comparison to the state-of-the-art metadynamics based conformer search as implemented in CREST. The comparison shows significant acceleration of up to an order of magnitude, while maintaining comparable accuracy. More importantly, the presented approach allows treatment of larger molecules than typically accessible with metadynamics.

Host-Guest Dynamic Behavior of Melatonin Encapsulated in β-Cyclodextrin Nanosponges

Cyclodextrin-based nanosponges are cross-linked polymeric porous nanomaterials obtained by condensation of cyclodextrins with a polyfunctional reagent (cross-linker). Owing to their high surface area, they are attractive for encapsulation applications aimed at increasing the stability, solubility, and bioavailability of drugs. Due to the structural complexity of these emerging materials, computer modeling can provide atomistic-level insights into both the flexibility of nanosponges and their interactions with encapsulated drugs. In this contribution, we focus on nanosponges of β-cyclodextrin cross-linked with citric acid and provide full-atom models for linear and cyclic topologies. We use extensive molecular dynamics (MD) simulations to analyze the flexibility of these constructs and their interactions with encapsulated melatonin, a neurohormone involved in sleep-wake cycle regulation also used as an antioxidant and immunomodulator. We characterize the main interactions responsible for melatonin binding and show that it benefits from multivalence and crowding effects.

Comparison between molecular dynamics potentials for simulation of graphene-based nanomaterials for biomedical applications

OBJECTIVE

This article provides a substantial review of recent research and comparison on molecular dynamics potentials to determine which are most suitable for simulating the phenomena in graphene-based nanomaterials (GBNs).

SIGNIFICANCE

GBNs gain significant attention due to their remarkable properties and potential applications, notably in nanomedicine. However, the physical and chemical characteristics toward macromolecules that justify their nanomedical applications are not yet fully understood. The molecular interaction through molecular dynamic simulation offers the benefits for simulating inorganic molecules like GBNs, with necessary adjustments to account for physical and chemical interactions, or thermodynamic conditions.

METHOD

In this review, we explore various molecular dynamics potentials (force fields) used to simulate interactions and phenomena in graphene-based nanomaterials. Additionally, we offer a brief overview of the benefits and drawbacks of each force fields that available for analysis to assess which one is suitable to study the molecular interaction of graphene-based nanomaterials.

RESULT

We identify and compare various molecular dynamics potentials that available for analyzing GBNs, providing insights into their suitability for simulating specific phenomena in graphene-based nanomaterials. The specification of each force fields and its purpose can be used for further application of molecular dynamics simulation on GBNs.

CONCLUSION

GBNs hold significant promise for applications like nanomedicine, but their physical and chemical properties must be thoroughly studied for safe clinical use. Molecular dynamics simulations, using either reactive or non-reactive MD potentials depending on the expected chemical changes, are essential for accurately modeling these properties, requiring careful selection based on the specific application.

Physics-Based Machine Learning to Predict Hydration Free Energies for Small Molecules with a Minimal Number of Descriptors: Interpretable and Accurate

Hydration free energy (HFE) of molecules is a fundamental property having importance throughout chemistry and biology. Calculation of the HFE can be challenging and expensive with classical molecular dynamics simulation-based approaches. Machine learning (ML) models are increasingly being used to predict HFE. Although the accuracy of ML models for data sets for small molecules is impressive, these models suffer from lack of interpretability. In this work, we have developed a physics-based ML model with only six descriptors, which is both accurate and fully interpretable, and applied it to a database for small molecule HFE, FreeSolv. We evaluated the electrostatic energy by an approximate closed form of the Generalized Born (GB) model and polar surface area. In addition, we have log P and hydrogen bond acceptor and donors as descriptors along with the number of rotatable bonds. We have used different ML models, such as random forest and extreme gradient boosting. The best result from these models has a mean absolute error of only 0.74 kcal/mol. The main power of this model is that the descriptors have clear physical meaning, and it was found that the descriptor describing the electrostatics and the polar surface area, followed by the hydrogen bond donors and acceptors, are the most important factors for the calculation of hydration free energy.

Grappa - a machine learned molecular mechanics force field

Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding. The resulting Grappa force field outperforms tabulated and machine-learned MM force fields in terms of accuracy at the same computational efficiency and can be used in existing Molecular Dynamics (MD) engines like GROMACS and OpenMM. It predicts energies and forces of small molecules, peptides, and RNA at state-of-the-art MM accuracy, while also reproducing experimentally measured values for J-couplings. With its simple input features and high data-efficiency, Grappa is well suited for extensions to uncharted regions of chemical space, which we show on the example of peptide radicals. We demonstrate Grappa's transferability to macromolecules in MD simulations from a small fast-folding protein up to a whole virus particle. Our force field sets the stage for biomolecular simulations closer to chemical accuracy, but with the same computational cost as established protein force fields.

Targeting histidinol-phosphate aminotransferase in Acinetobacter baumannii with salvianolic acid B: A structure-based approach to novel antibacterial strategies

Undoubtedly, Acinetobacter baumannii is a major ESKAPE pathogen that poses a significant threat to public health, causing severe nosocomial infections with high mortality rates in healthcare settings. Due to the rapid development of antibiotic resistance, only a limited number of antibiotics remain effective against infections caused by multidrug-resistant (MDR) Acinetobacter baumannii. The discovery of new class of antibiotic molecules still lags behind the rate of growing worldwide burden of antimicrobial resistance (AMR). To expedite the discovery of new therapeutic molecules, we have focused on HisC from A. baumannii (AbHisC), a crucial enzyme involved in the seventh step of histidine biosynthesis. This pathway is absent in humans. We have employed the advanced computational techniques to target this promising drug target. AbHisC was cloned, overexpressed, and purified. Three distinct sets of libraries containing ∼60,000 natural compounds from ZINC database were screened against AbHisC using Schrödinger's glide module software. Based on the docking score and glide energy, top 25 hits were further subjected to induced fit (IF) docking. Top four out of the twenty five compounds from IF docking were subjected to 100ns molecular dynamics simulations, and it was observed that salvianolic acid B (SA-B) (a naturally occurring compound) complex with AbHisC, was found to be extremely stable. The glide energy and docking score of SA-B were -88.59 kcal/mol and -10.4 kcal/mol. SA-B was also found to quench the intrinsic fluorescence of tyrosine indicating its binding to the target. The dissociation constant calculated using Surface Plasmon Resonance was found to be 3.4x10-9 M. Based on these results we can conclude that SA-B can serve as the potential inhibitor of AbHisC that can further form the basis of structure based drug design against this deadly pathogen.

Molecular basis for the diversification of lincosamide biosynthesis by pyridoxal phosphate-dependent enzymes

The biosynthesis of the lincosamide antibiotics lincomycin A and celesticetin involves the pyridoxal-5'-phosphate (PLP)-dependent enzymes LmbF and CcbF, which are responsible for bifurcation of the biosynthetic pathways. Despite recognizing the same S-glycosyl-L-cysteine structure of the substrates, LmbF catalyses thiol formation through β-elimination, whereas CcbF produces S-acetaldehyde through decarboxylation-coupled oxidative deamination. The structural basis for the diversification mechanism remains largely unexplored. Here we conduct structure-function analyses of LmbF and CcbF. X-ray crystal structures, docking and molecular dynamics simulations reveal that active-site aromatic residues play important roles in controlling the substrate binding mode and the reaction outcome. Furthermore, the reaction selectivity and oxygen-utilization of LmbF and CcbF were rationally engineered through structure- and calculation-based mutagenesis. Thus, the catalytic function of CcbF was switched to that of LmbF, and, remarkably, both LmbF and CcbF variants gained the oxidative-amidation activity to produce an unnatural S-acetamide derivative of lincosamide.

Structural and mechanism-based engineering of sulfotransferase CHST15 for the efficient synthesis of chondroitin sulfate E

Natural chondroitin sulfate (CS), extracted from animal cartilage, is widely used in the pharmaceuticals and foods. However, contamination with animal-derived heteropolysaccharides presents significant challenges, including potential immune responses. To address this, we developed a green and efficient method for synthesizing chondroitin sulfate E (CSE) via enzymatic synthesis, identifying EcCHST15, a sulfotransferase that catalyzes the conversion of chondroitin sulfate A (CSA) to CSE. We investigated the novel catalytic mechanism of CHST15 through quantum mechanical (QM) calculations and experimental validation, confirming its alignment with the SN2 reaction mechanism. Subsequently, we enhanced the catalytic efficiency of CHST15 using protein engineering, improving the catalytic efficiency from 18.1% in the wild type (WT) to 62.5% in the M7 mutant-a 3.5-fold increase. Finally, we constructed a six-enzyme cascade whole-cell catalyst, achieving a 72.2% conversion of 15 g/L CSA to produce CSE within 24 h. These findings offer a promising strategy for the industrial production of CSE.IMPORTANCECurrent methods for obtaining chondroitin sulfate (CS) primarily rely on tissue extraction and chemical synthesis. However, these approaches are hindered by contamination risks from animal-derived heteropolysaccharides and the technical challenges inherent in complex chemical synthesis, limiting the scalability of industrial CS production. To address this, we developed a green and efficient enzymatic biosynthesis method for chondroitin sulfate E (CSE). By identifying and engineering the sulfotransferase CHST15 from Erpetoichthys calabaricus, we created a mutant (EcCHST15M7) with a 3.5-fold increase in catalytic efficiency toward chondroitin sulfate A (CSA) compared to the wild-type enzyme. Additionally, we constructed a six-enzyme cascade whole-cell biocatalyst, achieving a 72.2% conversion rate from CSA to CSE. This study opens new avenues for the industrial-scale production of CSE through sustainable enzymatic processes.

Accurate Characterization of the Allosteric Energy Landscapes, Binding Hotspots and Long-Range Communications for KRAS Complexes with Effector Proteins : Integrative Approach Using Microsecond Molecular Dynamics, Deep Mutational Scanning of Binding Energetics and Allosteric Network Modeling

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Oncogenic mutations in KRAS, including G12V, G13D, and Q61R, drive constitutive activation and hyperactivation of signaling pathways, contributing to cancer progression. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS binding and allostery by which oncogenic mutations enhance KRAS-RAF1 binding and signaling remain incompletely understood. In this study, we employ microsecond molecular dynamics simulations, Markov State Modeling, mutational scanning and binding free energy calculations together with dynamic network modeling to elucidate the effect of KRAS mutations and characterize the thermodynamic and allosteric drivers and hotspots of KRAS binding and oncogenic activation. Our simulations revealed that oncogenic mutations stabilize the open active conformation of KRAS by differentially modulating the flexibility of the switch I and switch II regions, thereby enhancing RAF1 binding affinity. The G12V mutation rigidifies both switch I and switch II, locking KRAS in a stable, active state. In contrast, the G13D mutation moderately reduces switch I flexibility while increasing switch II dynamics, restoring a balance between stability and flexibility. The Q61R mutation induces a more complex conformational landscape, characterized by the increased switch II flexibility and expansion of functional macrostates, which promotes prolonged RAF1 binding and signaling. Mutational scanning of KRAS-RAF1 complexes identified key binding affinity hotspots, including Y40, E37, D38, and D33, and together with the MM-GBSA analysis revealed the hotspots leverage synergistic electrostatic and hydrophobic binding interactions in stabilizing the KRAS-RAF1 complexes. Network-based analysis of allosteric communication identifies critical KRAS residues (e.g., L6, E37, D57, R97) that mediate long-range interactions between the KRAS core and the RAF1 binding interface. The central β-sheet of KRAS emerges as a hub for transmitting conformational changes, linking distant functional sites and facilitating allosteric regulation. Strikingly, the predicted allosteric hotspots align with experimentally identified allosteric binding hotspots that define the energy landscape of KRAS allostery. This study highlights the power of integrating computational modeling with experimental data to unravel the complex dynamics of KRAS and its mutants. The identification of binding hotspots and allosteric communication routes offers new opportunities for developing targeted therapies to disrupt KRAS-RAF1 interactions and inhibit oncogenic signaling. Our results underscore the potential of computational approaches to guide the design of allosteric inhibitors and mutant-specific therapies for KRAS-driven cancers.

Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery

Lipid-mediated delivery of active pharmaceutical ingredients (API) opened new possibilities in advanced therapies. By encapsulating an API into a lipid nanocarrier (LNC), one can safely deliver APIs not soluble in water, those with otherwise strong adverse effects, or very fragile ones such as nucleic acids. However, for the rational design of LNCs, a detailed understanding of the composition-structure-function relationships is missing. This review presents currently available computational methods for LNC investigation, screening, and design. The state-of-the-art physics-based approaches are described, with the focus on molecular dynamics simulations in all-atom and coarse-grained resolution. Their strengths and weaknesses are discussed, highlighting the aspects necessary for obtaining reliable results in the simulations. Furthermore, a machine learning, i.e., data-based learning, approach to the design of lipid-mediated API delivery is introduced. The data produced by the experimental and theoretical approaches provide valuable insights. Processing these data can help optimize the design of LNCs for better performance. In the final section of this Review, state-of-the-art of computer simulations of LNCs are reviewed, specifically addressing the compatibility of experimental and computational insights.

Cysteine S-conjugate sulfoxide β-lyase activity for human ACCS

1-Aminocyclopropane-1-carboxylate synthase (ACCS) catalyzes the conversion of S-adenosyl-methionine to 1-aminocyclopropane-1-carboxylate (ACC), a rate-limiting step in ethylene biosynthesis. A gene encoding a putative ACCS protein was identified in the human genome two decades ago. It has been shown to not exhibit any canonical ACC synthase activity and its true function remains obscure. In this study, through a biochemical profiling approach, we demonstrate that human ACCS possesses cysteine conjugate sulfoxide β-lyase activity. This function is unexpected but reasonable, as it somewhat parallels the activity of ACCS proteins found in non-seed plants. Structure-function relationship study of human ACCS, guided by an AlphaFold2 model, allowed us to identify key active site residues that are important for its β-lyase activity. Our biochemical study of human ACCS also provided insights into the function of other mammalian ACCS homologs.

Enzymatic Degradation of Ochratoxin A: The Role of Ultra-Pure Water

Ochratoxin A (OTA) is a toxic mycotoxin, making its removal from food essential for public health. This study examines OTA degradation by porcine pancreatic lipase (PPL) in ultra-pure water versus buffer systems through in vitro assays and molecular modeling. The results show that PPL fully degrades OTA in ultra-pure water within 7 h at 44 °C, whereas only partial degradation occurs in phosphate buffer. After 4 h, PPL in water degrades 91% of OTA, compared to only 12% in buffer. The enzyme's half-life is longer in water (~4 h 4 min) than in phosphate buffer (~2 h 30 min), suggesting better stability in water. Other buffers, including acetate, citrate, and borate, confirmed higher degradation efficiency in low-conductivity, acidic environments similar to ultra-pure water. Additionally, using the model compound p-nitrophenyl octanoate (p-NPO), it was found that p-NPO degrades faster in buffer, likely due to a salting-out effect. Molecular modeling and circular dichroism analysis indicate that PPL's secondary structure in water promotes an ideal conformation for OTA binding. This study suggests ultra-pure water as a greener, sustainable option for reducing mycotoxins in food, with broad industrial applications.

Parallel Temperature Replica-Exchange Molecular Dynamics Simulations Capture the Observed Impact of Stapling on Coiled-Coil Conformational Stability

Macrocyclization or stapling is an important strategy for increasing the conformational stability and target-binding affinity of peptides and proteins, especially in therapeutic contexts. Atomistic simulations of such stapled peptides and proteins could help rationalize existing experimental data and provide predictive tools for the design of new stapled peptides and proteins. Standard approaches exist for incorporating nonstandard amino acids and functional groups into the force fields required for MD simulations and have been used in the context of stapling for more than a decade. However, enthusiasm for their use has been limited by their time-intensive nature and concerns about whether the resulting simulations would be physically realistic. Here, we report the development of force field parameters for two unnatural triazole staples, which we have incorporated into implicit-solvent parallel temperature replica-exchange molecular dynamics simulations of several stapled coiled-coil variants and their nonstapled counterparts. We used these simulations to calculate melting temperatures (Tm) of each variant along with the impact of stapling on the conformational stability of each variant relative to its nonstapled counterpart (ΔΔG). Trends among these simulated Tm and ΔΔG values closely match those observed in previous experiments, suggesting that the parameters we developed for these staples are sufficiently realistic to be useful in predicting the impact of stapling on the protein/peptide conformational stability in other contexts.

Control of Permanent Porosity in Type 3 Porous Liquids via Solvent Clustering

Porous liquids (PLs) are an exciting new class of materials for carbon capture due to their high gas adsorption capacity and ease of industrial implementation. They are composed of sorbent particles suspended in a nonadsorbed solvent, forming a liquid with permanent porosity. While PLs have a vast number of potential compositions based on the number of solvents and sorbent materials available, most of the research has been focused on the selection of the sorbent rather than the solvent. Therefore, PL design criteria on the supramolecular structures of the solvent are explored to create a fundamental understanding of how the solvent enables PL formation for rapid discovery of new PL compositions. Atomistic molecular dynamics simulation of eight solvents with a range of molecular sizes, shapes, and intramolecular bonding was performed, identifying that the shape and size of molecular clusters formed in the solvent are the driving predictor of PL formation rather than the size of the individual solvent molecule. The results demonstrate a significant departure from common approaches to PL formation based on the steric exclusion of solvent molecules from the sorbent via the size of the pore aperture. A modeling and experimental validation study further supports these findings. Through this computational material design study, a previously unexplored mechanism in PL formation, solvent-solvent clustering, is identified as a critical factor for the accelerated discovery of liquid phase carbon capture materials.

The evolution of the Amber additive protein force field: History, current status, and future

Molecular dynamics simulations are pivotal in elucidating the intricate properties of biological molecules. Nonetheless, the reliability of their outcomes hinges on the precision of the molecular force field utilized. In this perspective, we present a comprehensive review of the developmental trajectory of the Amber additive protein force field, delving into researchers' persistent quest for higher precision force fields and the prevailing challenges. We detail the parameterization process of the Amber protein force fields, emphasizing the specific improvements and retained features in each version compared to their predecessors. Furthermore, we discuss the challenges that current force fields encounter in balancing the interactions of protein-protein, protein-water, and water-water in molecular dynamics simulations, as well as potential solutions to overcome these issues.

Leveraging molecular dynamics, physicochemical, and structural analysis to explore OMP33-36 protein as a drug target in Acinetobacter baumannii: An approach against nosocomial infection

The Acinetobacter baumannii is a member of the "ESKAPE" bacteria responsible for many serious multidrug-resistant (MDR) illnesses. This bacteria swiftly adapts to environmental cues leading to the emergence of multidrug-resistant variants, particularly in hospital/medical settings. In this work, we have demonstrated the outer membrane protein 33-36 (Omp33-36) porin as a potential therapeutic target in A. baumannii and the regulatory potential of phytocompounds using an in-silico drug screening approach. Omp33-36 protein receptor was retrieved from the protein data bank and characterized as a receptor protein. The possible compounds (ligands) from three plants namely Andrographis paniculata, Cascabela thevetia, and Prosopis cineraria, were evaluated for their potential against bacterial infections based on prior investigations and selected for further analysis. Initially, seventy potential phytocompounds were identified and retrieved from IMPPAT database, followed by Physio-chemical characterizations and toxicity assessment using swissADME and ProTox server respectively. 15 compounds have shown significant drug-likeliness and were implemented for their interaction analysis with Omp33-36 using Autodock Vina. The docking study presented seven compounds with the best binding affinities, ranging from -7.2 kcal/mol to -7.9 kcal/mol and further, based on the potential of these compounds, 4 phytocompounds were introduced for molecular dynamic simulation for 200ns. During MD simulation, compounds Prosogerin, Quercitin and Tamarixetin have shown a substantial affinity for the Omp33-36 protein and binding energy ranging from -18 to -33 kcal/mol. Overall, the analysis depicted the two compounds, Quercitin and Tamarixetin, with the most consistent interactions and indicated promise as drug leads in regulating A. baumannii infection. However, in-vitro and in-vivo experimental validation are required to propose the selected phytomolecules as a therapeutic lead against A. baumannii.

Multiple Topology Replica Exchange of Expanded Ensembles for Multidimensional Alchemical Calculations

Relative free energy (RFE) calculations are now widely used in academia and the industry, but their accuracy is often limited by poor sampling of the complexes' conformational ensemble. To help address conformational sampling problems when simulating many relative binding free energies, we developed a novel method termed multiple topology replica exchange of expanded ensembles (MT-REXEE). This method enables parallel expanded ensemble calculations, facilitating iterative RFE computations while allowing conformational exchange between parallel transformations. These iterative transformations can be adaptable to any set of systems with a common backbone or central substructure. We demonstrate that the MT-REXEE method maintains thermodynamic cycle closure to the same extent as standard expanded ensemble calculations for both solvation free energy and relative binding free energy calculations. The transformations tested involve systems that incorporate diverse heavy atoms and multisite perturbations of a small molecule core resembling multisite λ dynamics, without necessitating modifications to the MD code. Our initial implementation is in GROMACS. We outline a systematic approach for the topology setup and provide instructions on how to perform inter-replica coordinate modifications. This work shows that MT-REXEE can be used to perform accurate and reproducible free energy estimates and prompts expansion to more complex test systems and other molecular dynamics simulation infrastructures.

Discovery of PRMT5 N-Terminal TIM Barrel Ligands from Machine-Learning-Based Virtual Screening

Protein arginine methyltransferase 5 (PRMT5), which symmetrically dimethylates cytosolic and nuclear proteins, has been demonstrated as an important cancer therapeutic target. In recent years, many advanced achievements in PRMT5 inhibitor development have been made. Most PRMT5 inhibitors in the clinical trial focus on targeting the C-terminal catalytic domain, whereas developing small molecules to interrupt the PRMT5/pICLn (methylosome subunit) protein-protein interface is also of great importance for inhibiting PRMT5. Here, we describe a machine-learning-based virtual screening method and use this novel pipeline to screen small-molecule inhibitors of the PRMT5/pICLn interaction. 18 compounds were manually selected for experimental testing. One compound, Z319334062, showed surface plasmon resonance-binding affinity to the target (K D = 21.5 μM) and dose-dependently inhibited symmetric dimethylation levels in patient-derived glioblastoma cell lines.

Predicting the polyspecificity of aminoacyl-tRNA synthetase for non-canonical amino acids using molecular dynamics simulation and MM/PBSA

With the advancement of genetic code expansion, the field is progressing towards incorporating multiple non-canonical amino acids (ncAAs). The specificity of aminoacyl-tRNA synthetases (aaRSs) towards ncAAs is a critical factor, as engineered aaRSs frequently show polyspecificity, complicating the precise incorporation of multiple ncAAs. To address this challenge, predicting binding affinity can be beneficial. In this study, we expressed sfGFP using an orthogonal aaRS/tRNA pair with 4-Azido-L-phenylalanine (AzF) and another four different ncAAs. The experimental results showed specificity with O-Methyl-L-tyrosine as well as AzF, and these results were compared with computational predictions. We constructed a mutant aaRS structure specific for AzF through homology modelling and conducted docking studies with tyrosine and five ncAAs, followed by molecular dynamics simulations. The binding affinity was calculated using the molecular mechanics/Poisson-Boltzmann surface area, focusing on nonpolar solvation terms. While the analysis is based on the incorporation of limited number of ncAAs, the cavity and dispersion term method showed consistency with experimental data, highlighting its potential utility compared to the surface area term method. These findings enhance understanding of the ncAA specificity of aaRS in relation to computer simulations and energy calculations, which can be utilized to rationally design or predict the specificity of aaRS.

Interactions Determining Stereoselectivity in Two-Dimensional Systems─The Case of 22-Hydroxycholesterol Epimers

Oxidized derivatives of cholesterol play an important role in the functioning of biomembranes. Unlike other biomolecules, which are physiologically active in only one enantiomeric form, some oxysterols exist endogenously as two stereoisomers that exhibit strictly different biological effects. In this paper, we focused our attention on 22-hydroxycholesterol (22-OH) epimers, 22(R)-OH and 22(S)-OH, and examined their properties in Langmuir monolayers spread at the air/water interface, using classical surface manometry complemented with Brewster angle microscopy (BAM) images of the film texture. The studied epimers showed quite different monolayer characteristics. Namely, 22(S)-OH formed homogeneous, condensed monolayers of high collapse pressure, while 22(R)-OH films were more disordered and expanded with quite low collapse pressure. Interestingly, the latter compound showed in the course of the surface pressure-molecular area isotherm a temperature-dependent plateau transition, characterized by the coexistence of domains of molecules with different inclinations, visible in BAM images as patches of varying brightness. Molecular dynamics (MD) simulations confirmed this and revealed that the greater structural variability of 22(R)-OH is due to the greater hydration of the oxygen atom at C(22) compared to the other epimer. Next, we tested whether 22-OH epimers could differentiate interactions with sphingomyelin (SM). Although the strength of interaction with SM was similar for both epimers, the composition of the films corresponding to this minimum was different. With the aid of MD, it was found that these differences result directly from the interplay between 22-OH molecules and their ability for hydrogen bond formation. Therefore, the stereochemistry of oxysterols seems to play a crucial role in the overall structural organization of the membrane.

An integrative analysis reveals cancer risk associated with artificial sweeteners

BACKGROUND

Artificial sweeteners (AS) have been widely utilized in the food, beverage, and pharmaceutical industries for decades. While numerous publications have suggested a potential link between AS and diseases, particularly cancer, controversy still surrounds this issue. This study aims to investigate the association between AS consumption and cancer risk.

METHODS

Targets associated with commonly used AS were screened and validated using databases such as CTD, STITCH, Super-PRED, Swiss Target Prediction, SEA, PharmMapper, and GalaxySagittarius. Cancer-related targets were sourced from GeneCards, OMIM, and TTD databases. AS-cancer targets were identified through the intersection of these datasets. A network visualization ('AS-targets-cancer') was constructed using Cytoscape 3.9.0. Protein-protein interaction analysis was conducted using the STRING database to identify significant AS-cancer targets. GO and KEGG enrichment analyses were performed using the DAVID database. Core targets were identified from significant targets and genes involved in the 'Pathways in cancer' (map05200). Molecular docking and dynamics simulations were employed to verify interactions between AS and target proteins. Pan-cancer and univariate Cox regression analyses of core targets across 33 cancer types were conducted using GEPIA 2 and SangerBox, respectively. Gene chip datasets (GSE53757 for KIRC, GSE21354 for LGG, GSE42568 for BRCA, and GSE46602 for PRAD) were retrieved from the GEO database, while transcriptome and overall survival data were obtained from TCGA. Data normalization and identification of differentially expressed genes (DEGs) were performed on these datasets using R (version 4.3.2). Gene Set Enrichment Analysis (GSEA) was employed to identify critical pathways in the gene expression profiles between normal and cancer groups. A cancer risk prognostic model was constructed for key targets to further elucidate their significance in cancer initiation and progression. Finally, the HPA database was utilized to investigate variations in the expression of key AS-cancer target proteins across KIRC, LGG, BRCA, PRAD, and normal tissues.

RESULTS

Seven commonly used AS (Aspartame, Acesulfame, Sucralose, NHDC, Cyclamate, Neotame, and Saccharin) were selected for study. A total of 368 AS-cancer intersection targets were identified, with 48 notable AS-cancer targets, including TP53, EGFR, SRC, PIK3R1, and EP300, retrieved. GO biological process analysis indicated that these targets are involved in the regulation of apoptosis, gene expression, and cell proliferation. Thirty-five core targets were identified from the intersection of the 48 significant AS-cancer targets and genes in the 'Pathways in cancer' (map05200). KEGG enrichment analysis of these core targets revealed associations with several cancer types and the PI3K-Akt signaling pathway. Molecular docking and dynamics simulations confirmed interactions between AS and these core targets. HSP90AA1 was found to be highly expressed across the 33 cancer types, while EGF showed the opposite trend. Univariate Cox regression analysis demonstrated strong associations of core targets with KIRC, LGG, BRCA, and PRAD. DEGs of AS-cancer core targets across these four cancers were analyzed. GSEA revealed upregulated and downregulated pathways enriched in KIRC, LGG, BRCA, and PRAD. Cancer risk prognostic models were constructed to elucidate the significant roles of key targets in cancer initiation and progression. Finally, the HPA database confirmed the crucial function of these targets in KIRC, LGG, BRCA, and PRAD.

CONCLUSION

This study integrated data mining, machine learning, network toxicology, molecular docking, molecular dynamics simulations, and clinical sample analysis to demonstrate that AS increases the risk of kidney cancer, low-grade glioma, breast cancer, and prostate cancer through multiple targets and signaling pathways. This paper provides a valuable reference for the safety assessment and cancer risk evaluation of food additives. It urges food safety regulatory agencies to strengthen oversight and encourages the public to reduce consumption of foods and beverages containing artificial sweeteners and other additives.

Interactions of sphingomyelin with biologically crucial side chain-hydroxylated cholesterol derivatives

Oxysterols are interesting molecules due to their dual nature, reflecting beneficial and harmful effects on the body. An issue that still needs to be solved is how slight modification of their structure owing to the location of the additional polar group in the molecules affects their biological activity. With this in mind, we selected three side chain-hydroxylated oxysterols namely: 20(S)-hydroxycholesterol (20(S)-OH), 24(S)-hydroxycholesterol (24(S)-OH), and 27-hydroxycholesterol (27-OH), and examined their behavior in mixtures with the bioactive sphingolipid - sphingomyelin (SM). Our research was based on the Langmuir monolayer technique supplemented with molecular dynamics (MD) and microscopic observation of the films texture (Brewster angle microscopy, BAM, and atomic force microscopy, AFM). Additionally, since 20(S)-hydroxycholesterol has not been studied so far, we thoroughly characterized this oxysterol in one-component monolayers. Our studies showed differences in the interactions of the studied oxysterols and sphingomyelin. Namely, it was found that 20(S)-OH binds to SM, unlike 24(S)-OH and 27-OH, which both weakly interact with SM. This distinct behavior was interpreted within the molecular dynamics as being due to weak intermolecular interactions between 20(S)-OH molecules, which allowed easy incorporation of SM into the 20(S)-OH monolayer. In contrast, the strong oxysterol-oxysterol interactions occurring in monolayers with 24(S)-OH or 27-OH make this process more difficult. This may be important in the process of bone formation/resorption. Other aspects derived from our study are: (i) the tendency of oxysterols to incorporate into lipid rafts (leading to their modification in structure and function), as well as (ii) the formation of multilayer structures, in which oxysterols are arranged in the characteristic forms of "strings of beads", which may facilitate their transport across the membrane.

Balancing G protein selectivity and efficacy in the adenosine A2A receptor

The adenosine A2A receptor (A2AR) engages several G proteins, notably Go and its cognate Gs protein. This coupling promiscuity is facilitated by a dynamic ensemble, revealed by 19F nuclear magnetic resonance imaging of A2AR and G protein. Two transmembrane helix 6 (TM6) activation states, formerly associated with partial and full agonism, accommodate the differing volumes of Gs and Go. While nucleotide depletion biases TM7 toward a fully active state in A2AR-Gs, A2AR-Go is characterized by a dynamic inactive/intermediate fraction. Molecular dynamics simulations reveal that the NPxxY motif, a highly conserved switch, establishes a unique configuration in the A2AR-Go complex, failing to stabilize the helix-8 interface with Gs, and adoption of the active state. The resulting TM7 dynamics hamper G protein coupling, suggesting kinetic gating may be responsible for reduced efficacy in the noncognate G protein complex. Thus, dual TM6 activation states enable greater diversity of coupling partners while TM7 dynamics dictate coupling efficacy.

Crystal structure of a novel heterooligomeric aminotransferase from Serratia sp. ATCC 39006 provides insights into function

Serratia sp. ATCC 39006 has two tandemly positioned genes, ser4 and ser5, both annotated as sugar aminotransferases, in a putative secondary metabolite biosynthetic gene cluster. Ser5 possesses a complete fold-type I aminotransferase fold, while Ser4 lacks the N- and C-terminal regions and a catalytically important lysine residue of fold-type I aminotransferase. We herein revealed that Ser4 and Ser5 formed a heterotetrameric complex (SerTA) with aminotransferase activity and determined the crystal structures. MD simulations and activity assays with SerTA variants indicated that residues from helix α-8* of inactive Ser4 are important for activity, confirming the importance of heterocomplex formation for activity. Furthermore, the structures suggest that SerTA recognizes a substrate loaded on the carrier protein.

Glycated walnut meal peptide‑calcium chelates (COS-MMGGED-Ca): Preparation, characterization, and calcium absorption-promoting

This study isolated a novel peptide MMGGED with strong calcium-binding capacity from defatted walnut meal and synthesized a novel peptide‑calcium chelate COS-MMGGED-Ca with high stability via glycation. Structural characterization and computer simulation identified binding sites, while in vitro digestion stability and calcium transport experiments explored the chelate's properties. Results showed that after glycation, COS-MMGGED bound Ca2+ with 88.75 ± 1.75 %, mainly via aspartic and glutamic acids. COS-MMGGED-Ca released Ca2+ steadily (60.27 %), with thermal denaturation temperature increased by 18 °C and 37 °C compared to MMGGED-Ca, indicating good processing performance. Furthermore, COS-MMGGED significantly enhanced Ca2+ transport across Caco-2 monolayers, 1.13-fold and 1.62-fold higher than CaCl2 and MMGGED, respectively, at 240 h. These findings prove glycation enhances structural properties, stability, calcium loading, and transport of peptide‑calcium chelates, providing a scientific basis for developing novel efficient calcium supplements and high-value utilization of walnut meal.

Identification and study of new NF-κB-inducing kinase ligands derived from the imidazolone scaffold

Chronic kidney disease (CKD) is a growing health concern, projected to be a major cause of death by 2040, due to an increasing risk of acute kidney injury (AKI). Systems biology-derived data suggest that the unmet need for an orally available drug to treat AKI and improve CKD outcomes may be addressed by targeting kidney inflammation and, specifically, nuclear factor κB-inducing kinase (NIK), a key signaling molecule that activates the noncanonical nuclear factor κB (NF-κB) pathway. We have prepared and identified a small family of imidazolone derivatives that bind NIK and inhibit the noncanonical NF-κB activation pathway. The introduction of heterocyclic substituents in position 2 of the imidazolone core provides compounds with affinity against human NIK. Three candidates, with best affinity profile, were tested in phenotypic experiments of noncanonical NF-κB activation, confirming that the derivative bearing the 4-pyridyl ring can inhibit the processing of NFκB p100 to NFkB2 p52, which is NIK-dependent in cultured kidney tubular cells. Finally, exhaustive docking calculations combined with molecular dynamics studies led us to propose a theoretical binding mode and rationalize affinity measures, in which the aminopyridine motif is a key anchoring point to the hinge region thanks to several hydrogen bonds and the interaction of heterocyclic rings in position 2 with Ser476 and Lys482. Our result will pave the way for the development of potential drug candidates targeting NIK in the context of CKD.

Protocols for Metallo- and Serine-β-Lactamase Free Energy Predictions: Insights from Cross-Class Inhibitors

While relative binding free energy (RBFE) calculations using alchemical methods are routinely carried out for many pharmaceutically relevant protein targets, challenges remain. For example, open-source tools do not support the easy setup and simulation of metalloproteins, particularly when ligands directly coordinate to the metal site. Here, we evaluate the performance of RBFE methods for KPC-2, a serine-β-lactamase (SBL), and two nonbonded metal parameter setups for VIM-2, a metallo-β-lactamase (MBL) with two active site zinc ions. We tested two different ways of modeling the ligand-zinc interactions. First, a restraint-based approach, in which FF14SB zinc parameters are combined with harmonic restraints between the zincs and their coordinating residues. The second approach uses an upgraded Amber force field (UAFF) for zinc-metalloproteins with adjusted partial charges and nonbonded terms of zinc-coordinating residues. Molecular mechanics (MM) and quantum mechanics/molecular mechanics (QM/MM) simulations show that the crystallographically observed zinc coordination is not retained in MM simulations with either zinc parameter set for a series of known phosphonic acid-based inhibitors bound to VIM-2. These phosphonic acid-based inhibitors exhibit known cross-class affinity for SBLs and MBLs and serve as a benchmark for RBFE calculations for VIM-2, after validation with KPC-2. The KPC-2 free energy of binding estimates are within expected literature accuracies for the ligand series with a mean absolute error of 0.45 0.28 0.66 kcal/mol and a Pearson's correlation coefficient of 0.93 0.85 0.98 . For VIM-2, the UAFF approach has improved correlation from 0.55 - 0.04 0.88 to 0.78 0.38 0.92 , compared to the restraint approach. The presented strategies for handling ligands coordinating to metal sites highlight that simple metal parameter models can provide some predictive free energy estimates for metalloprotein-ligand systems, but leave room for improvement in their ease of use, modeling of coordination sites and as a result, their accuracy.

DNA sequence and lesion-dependent mitochondrial transcription factor A (TFAM)-DNA-binding modulates DNA repair activities and products

Mitochondrial DNA (mtDNA) is indispensable for mitochondrial function and is maintained by DNA repair, turnover, mitochondrial dynamics and mitophagy, along with the inherent redundancy of mtDNA. Base excision repair (BER) is a major DNA repair mechanism in mammalian mitochondria. Mitochondrial BER enzymes are implicated in mtDNA-mediated immune response and inflammation. mtDNA is organized into mitochondrial nucleoids by mitochondrial transcription factor A (TFAM). The regulation of DNA repair activities by TFAM-DNA interactions remains understudied. Here, we demonstrate the modulation of DNA repair enzymes by TFAM concentrations, DNA sequences and DNA modifications. Unlike previously reported inhibitory effects, we observed that human uracil-DNA glycosylase 1 (UNG1) and AP endonuclease I (APE1) have optimal activities at specific TFAM/DNA molar ratios. High TFAM/DNA ratios inhibited other enzymes, OGG1 and AAG. In addition, TFAM reduces the accumulation of certain repair intermediates. Molecular dynamics simulations and DNA-binding experiments demonstrate that the presence of 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) in certain sequence motifs enhances TFAM-DNA binding, partially explaining the inhibition of OGG1 activity. Bioinformatic analysis of published 8-oxodG, dU, and TFAM-footprint maps reveals a correlation between 8-oxodG and TFAM locations in mtDNA. Collectively, these results highlight the complex regulation of mtDNA repair by DNA sequence, TFAM concentrations, lesions and repair enzymes.

Unraveling the Molecular Reason of Opposing Effects of α-Mangostin and Norfluoxetine on TREK-2 at the Same Binding Site

TWIK-related K+ channel (TREK)-2, expressed in sensory neurons, is involved in setting membrane potential, and its modulations contributes to the generation of nociceptive signals. Although acute and chronic pain is a common symptom experienced by patients with various conditions, most existing analgesics exhibit low efficacy and are associated with adverse effects. For this reason, finding the novel modulator of TREK-2 is of significance for the development of new analgesics. Recent studies have shown that α-Mangostin (α-MG) activates TREK-2, facilitating analgesic effects, yet the underlying molecular mechanisms remain elusive. Intriguingly, even though norfluoxetine (NFx) is known to inhibit TREK-2, α-MG is also observed to share a same binding site with NFx, and this implies that TREK-2 might be modulated in a highly complicated manner. Therefore, we examine the mechanism of how TREK-2 is activated by α-MG using computational methods and patch clamp experiments in the present study. Based on these results, we offer an explanation of how α-MG and NFx exhibit opposing effects at the same binding site of TREK-2. These findings will broaden our understanding of TREK-2 modulation, providing clues for designing novel analgesic drugs.

Enriching productive mutational paths accelerates enzyme evolution

Darwinian evolution has given rise to all the enzymes that enable life on Earth. Mimicking natural selection, scientists have learned to tailor these biocatalysts through recursive cycles of mutation, selection and amplification, often relying on screening large protein libraries to productively modulate the complex interplay between protein structure, dynamics and function. Here we show that by removing destabilizing mutations at the library design stage and taking advantage of recent advances in gene synthesis, we can accelerate the evolution of a computationally designed enzyme. In only five rounds of evolution, we generated a Kemp eliminase-an enzymatic model system for proton transfer from carbon-that accelerates the proton abstraction step >108-fold over the uncatalyzed reaction. Recombining the resulting variant with a previously evolved Kemp eliminase HG3.17, which exhibits similar activity but differs by 29 substitutions, allowed us to chart the topography of the designer enzyme's fitness landscape, highlighting that a given protein scaffold can accommodate several, equally viable solutions to a specific catalytic problem.

New model compounds for the efficient colorimetric screening of medium chain length polyhydroxyalkanoate (mcl-PHA) depolymerases reveal mechanism of activity

Plastic pollution presents a significant environmental problem contributing to increased CO2 emissions and persistently accumulation in ecosystems. Biobased polymers, like polyhydroxyalkanoates (PHAs), offer a part of a solution with their biodegradability and reduced carbon footprint. However, effective end-of-life strategies, such as controlled enzymatic depolymerization, are crucial for sustainability, relying on efficient PHA depolymerases (PHAases). Here we describe the synthesis of two new chromogenic compounds derived from polyhydroxyoctanoate (PHO) and their application in a continuous, quantitative spectrophotometric assay for PHO depolymerase and other medium chain lengths PHAase activity within 10 min. These substrates allow activity measurement at temperatures above 45 °C, simplifying the comparison of PHAases and aiding enzymatic degradation progress. The study also explores enzyme specificity and identifies key amino acids involved in PHO recognition by PfPHOase. The 3-hydroxyoctanoyl moieties of both compounds were found to bind specifically to a groove formed by the amino acids Phe96, Phe125, Ile171, and Val230, which are highly conserved in known mcl-PHA depolymerases.

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183

Contemporary research has convincingly demonstrated that upregulation of G protein-coupled receptor 183 (GPR183), orchestrated by its endogenous agonist, 7α,25-dihydroxyxcholesterol (7α,25-OHC), leads to the development of cancer, diabetes, multiple sclerosis, infectious, and inflammatory diseases. A recent study unveiled the cryo-EM structure of 7α,25-OHC bound GPR183 complex, presenting an untapped opportunity for computational exploration of potential GPR183 inhibitors, which served as our inspiration for the current work. A predictive and validated two-dimensional QSAR model using genetic algorithm (GA) and multiple linear regression (MLR) on experimental GPR183 inhibition data was developed. QSAR study highlighted that structural features like dissimilar electronegative atoms, quaternary carbon atoms, and CH2RX fragment (X: heteroatoms) influence positively, while the existence of oxygen atoms with a topological separation of 3, negatively affects GPR183 inhibitory activity. Post assessment of true external set prediction capability, the MLR model was deployed to screen 12,449 DrugBank compounds, followed by a screening pipeline involving molecular docking, druglikeness, ADMET, protein-ligand stability assessment using deep learning algorithm, molecular dynamics, and molecular mechanics. The current findings strongly evidenced DB05790 as a potential lead for prospective interference of oxysterol-mediated GPR183 overexpression, warranting further in vitro and in vivo validation.

Development of an efficient NUPR1 inhibitor with anticancer activity

Pancreatic cancer is highly lethal and has limited treatment options available. Our team had previously developed ZZW-115, a promising drug candidate that targets the nuclear protein 1 (NUPR1), which is involved in pancreatic cancer development and progression. However, clinical translation of ZZW-115 was hindered due to potential cardiotoxicity caused by its interaction with the human Ether-à-go-go-Related Gene (hERG) potassium channel. To address this, we have performed a high-throughput screening of 10,000 compounds from the HitFinder Chemical Library, and identified AJO14 as a lead compound that binds to NUPR1, without having favorable affinity towards hERG. AJO14 induced cell death through apoptosis, necroptosis, and parthanatos (induced by the poly-ADP ribose polymerase (PARP) overactivation), driven by mitochondrial catastrophe and decreased ATP production. This process seemed to be mediated by the hyperPARylation (an excessive modification of proteins by PARP, leading to cellular dysfunction), as it could be reversed by Olaparib, a PARP inhibitor. In xenografted mice, AJO14 demonstrated a dose-dependent tumor reduction activity. Furthermore, we attempted to improve the anti-cancer properties of AJO14 by molecular modification of the lead compound. Among the 51 candidates obtained and tested, 8 compounds exhibited a significant increase in efficacy and have been retained for further studies, especially LZX-2-73. These AJO14-derived compounds offer potent NUPR1 inhibition for pancreatic cancer treatment, without cardiotoxicity concerns.

An Interdisciplinary Approach Provides Insights into the Pronounced Selectivity of Compound 42 for DPP9

Dipeptidyl peptidase 8 (DPP8) and 9 (DPP9) are proteases gaining significant attention for their role in health and disease. Distinctive studies of these proteases are hampered by their close homology. Furthermore, designing selective compounds is a major challenge due to the highly conserved catalytic site. Here, we provide mechanistic insights underlying the DPP9-over-DPP8 selectivity of the semi-selective inhibitor "Compound 42". We performed enhanced sampling molecular dynamics simulations to investigate the binding pose of "Compound 42", which enabled the design of various DPP9 mutants that were characterized through a combination of biochemical (Ki determinations) and in silico approaches. Our findings show that DPP9 residue F253 is an important selectivity-determining factor. This work marks the discovery and validation of a structural feature that can be exploited for the design of DPP8 or DPP9 selective inhibitors.

Design, synthesis, biological evaluation, and molecular modeling studies of some quinazolin-4(3H)-one-benzenesulfonamide hybrids as potential α-glucosidase inhibitors

Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia, posing serious health risks and becoming increasingly prevalent. Prolonged hyperglycemia can lead to complications such as nephropathy, neuropathy, retinopathy, cardiovascular damage, and blindness. Controlling hyperglycemia through α-glucosidase inhibitors, which slow down carbohydrate breakdown, is an effective treatment strategy. However, current inhibitors like acarbose, voglibose, and miglitol while used to manage type 2 diabetes, have significant side effects. Therefore, developing new α-glucosidase inhibitors that are more effective and have fewer side effects is crucial. In this study, a series of novel quinazolin-4(3H)-one-benzenesulfonamide hybrid compounds were designed, synthesized, and evaluated for in vitro α-glucosidase inhibitory activity. The compounds showed higher enzyme inhibition potency, with IC50 values ranging between 129.2 ± 0.5 and 558.7 ± 13.7 µM, compared to acarbose (IC50=814.3 ± 13.5 µM). Among the tested compounds, compound 10, bearing a 4-chlorophenyl ring on the nitrogen atom of the sulfonamide group, was the most active, with an IC50 value of 129.2 ± 0.5 µM. Enzyme kinetics analyses and molecular modeling studies were conducted to understand their inhibition mechanisms and interactions with the enzyme. The kinetic studies revealed a mixed-type inhibition model, indicating that the compounds bind to the enzyme-substrate complex with higher affinity than to the free enzyme. Molecular modeling results confirmed these findings. Additionally, in silico prediction studies showed that the selected compounds have favourable physicochemical and drug-like properties. These results suggest these compounds have potential for further optimization and development as effective α-glucosidase inhibitors for diabetes treatment.

Structural basis of tolvaptan binding to the vasopressin V2 receptor

The vasopressin V2 receptor (V2R) is a validated therapeutic target for autosomal dominant polycystic kidney disease (ADPKD), with tolvaptan being the first FDA-approved antagonist. Herein, we used Gaussian accelerated molecular dynamics simulations to investigate the spontaneous binding of tolvaptan to both active and inactive V2R conformations at the atomic-level. Overall, the binding process consists of two stages. Tolvaptan binds initially to extracellular loops 2 and 3 (ECL2/3) before overcoming an energy barrier to enter the pocket. Our simulations result highlighted key residues (e.g., R181, Y205, F287, F178) involved in this process, which were experimentally confirmed by site-directed mutagenesis. This work provides structural insights into tolvaptan-V2R interactions, potentially aiding the design of novel antagonists for V2R and other G protein-coupled receptors.

Investigating the role of PmrB mutation on Colistin antibiotics drug resistance in Klebsiella Pneumoniae

Klebsiella pneumoniae, a member of the Enterobacteriaceae family, naturally resides in the digestive tracts of both healthy animals and humans. Carbapenem-resistant Klebsiella pneumoniae (CRKP) poses a significant health risk for hospitalized patients worldwide, greatly reducing the effectiveness of commonly used antibiotics. This leaves healthcare providers with limited treatment options, often relying on colistin. PmrB is important for the survival of Klebsiella pneumonia and, a mutation in the PmrB protein is accountable for the development of colistin antibiotic resistance in Klebsiella pneumonia. This is especially important because colistin is a fundamental component in the treatment of pneumonia. Three mutated residues-T157P, G207D, and T246A-are responsible for colistin resistance. The structural alterations and underlying mechanisms in the PmrB protein that cause resistance owing to mutation remain unclear. As a result, this study is focused to the exploration of the putative mechanism of resistance resulting from these mutations, as well as the structure modification of normal and mutant PmrB proteins, using molecular docking and molecular dynamics simulations analysis. Our results demonstrated that the interaction paradigm for the mutants has been altered and thus showing a significant effect upon the hydrogen bonding network. Interestingly, the binding of Colistin with the three mutant demonstrate unstable behavior as compared with WT+Colistin. The proposed drug-resistance mechanism will help to guide the development of PmrB drugs. These finding may give a new framework for developing novel drugs against the mutant version of PmrB.

Discovery of novel NLRP3 inhibitors based on machine learning and physical methods

The NLRP3 inflammasome plays a crucial role in inflammatory responses, particularly in alcohol-related liver disease (ALD). Given that NLRP3 has emerged as a potential therapeutic target for ALD, the development of effective inhibitors is of great importance. In this study, we trained 11 regression models, and the results showed that LightGBM, Random Forest, and XGBoost performed the best, achieving R² values of 0.774, 0.755, and 0.719, respectively. Using machine learning models and physical methods, we screened more than 11.5 million compounds from Asinex, Princeton, UkrOrgSynthesis, Chemdiv, Chembridge, Alinda, Enamine, and Lifechemicals, which led to the identification of 26 potential NLRP3 inhibitors. Furthermore, molecular dynamics simulations and MMGBSA binding energy calculations confirmed the stability of the interactions between NLRP3 and three key molecules: 19,655,631 (source Chembridge), 38,214,692 (source Chembridge), and Z1180203703 (source Enamine). Additionally, ADMET analysis revealed their favorable pharmacokinetic properties. This study provides insights and candidate molecules for discovering NLRP3 inhibitors, potentially applicable in treating related diseases.

NetSci: A Library for High Performance Biomolecular Simulation Network Analysis Computation

We present the NetSci program-an open-source scientific software package designed for estimating mutual information (MI) between data sets using GPU acceleration and a k-nearest-neighbor algorithm. This approach significantly enhances calculation speed, achieving improvements of several orders of magnitude over traditional CPU-based methods, with data set size limits dictated only by available hardware. To validate NetSci, we accurately compute MI for an analytically verifiable two-dimensional Gaussian distribution and replicate the generalized correlation (GC) analysis previously conducted on the B1 domain of protein G. We also apply NetSci to molecular dynamics simulations of the Sarcoendoplasmic Reticulum Calcium-ATPase (SERCA) pump, exploring the allosteric mechanisms and pathways influenced by ATP and 2'-deoxy-ATP (dATP) binding. Our analysis reveals distinct allosteric effects induced by ATP compared to dATP, with predicted information pathways from the bound nucleotide to the calcium-binding domain differing based on the nucleotide involved. NetSci proves to be a valuable tool for estimating MI and GC in various data sets and is particularly effective for analyzing intraprotein communication and information transfer.